A Machine Learning-based Career Recommendation
Year: 2024
Type: article
Abstract: Career selection is perhaps the most significant decision any student makes during their academic journey. This research thus presents a machine learning-based career recommendation system that will offer each student a career suggestion based on their academic performance and extracurricular involvement, including whether they hold a part-time job. Evaluations were conducted on several supervised machine learning models for predicting best career paths, such as Random Forest, Support Vector machine (SVM), and K-Nearest Neighbor (KNN) . Experiments revealed that Random Forest performed best and had an accuracy of 93%. The proposed system assists students in making informed career decisions based on data analysis.
Authors: Vaishnavi Nayak, Neha Vora
Institution: Shri Vile Parle Kelavani Mandal
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404869420